Early stimulated immune responses predict clinical disease severity in hospitalized COVID-19 patients

Background The immune pathogenesis underlying the diverse clinical course of COVID-19 is poorly understood. Currently, there is an unmet need in daily clinical practice for early biomarkers and improved risk stratification tools to help identify and monitor COVID-19 patients at risk of severe disease. Methods We performed longitudinal assessment of stimulated immune responses in 30 patients hospitalized with COVID-19. We used the TruCulture whole-blood ligand-stimulation assay applying standardized stimuli to activate distinct immune pathways, allowing quantification of cytokine responses. We further characterized immune cell subsets by flow cytometry and used this deep immunophenotyping data to map the course of clinical disease within and between patients. Results Here we demonstrate impairments in innate immune response pathways at time of COVID-19 hospitalization that are associated with the development of severe disease. We show that these impairments are transient in those discharged from hospital, as illustrated by functional and cellular immune reconstitution. Specifically, we identify lower levels of LPS-stimulated IL-1β, and R848-stimulated IL-12 and IL-17A, at hospital admission to be significantly associated with increasing COVID-19 disease severity during hospitalization. Furthermore, we propose a stimulated immune response signature for predicting risk of developing severe or critical COVID-19 disease at time of hospitalization, to validate in larger cohorts. Conclusions We identify early impairments in innate immune responses that are associated with subsequent COVID-19 disease severity. Our findings provide basis for early identification of patients at risk of severe disease which may have significant implications for the early management of patients hospitalized with COVID-19.


Clinical data
The definition of a pre-existing condition associated with immune suppression was established together with a team of specialist clinicians, by assessment of each patients' medical history including diagnoses, medications and previous hospital admissions. For the current cohort, pre-existing immunosuppressive conditions included active malignancy (solid or hematological) with either chemotherapy regime administered within 12 months prior or having undergone substantial surgery within 1 month prior, seropositive rheumatoid arthritis with methotrexate-associated lung fibrosis, active human immunodeficiency virus infection/acquired immunodeficiency syndrome, chronic kidney disease ³ grade 4, receiving immunosuppressive medication due to a solid organ transplant, or severe comorbidity with chronic insufficiency of multiple organs (severe chronic cardiac insufficiency, severe chronic obstructive pulmonary disease , and chronic kidney disease).

Defining a clinical severity scale
Based on the clinical disease trajectories, we defined a clinical severity scale with 4 grades of disease severity (Fig 1C), modified from a previously published COVID-19 severity grading system. 24 Where the World Health Organization (WHO) COVID-19 disease severity classifications 35 are based mainly on the symptoms of the patient, the severity scale applied here was based on needed interventions. Grade 1 was defined as requiring less than 3 liters per minute (L/min) of supplemental oxygen (O2) to keep peripheral blood oxygen saturation (SAT) > 92%, and would correspond mainly to WHO classification mild-and moderate disease, however may also include milder cases of WHO classification severe disease. Grade 2 was defined as requiring ³3 but <6 L/min of O2 to maintain SAT > 92%, and would correspond mainly to WHO classification severe disease. Grade 3 was defined as requiring ³6 L/min of O2 and/or being admitted to the ICU, and would correspond to more severe cases of WHO classification severe disease as well as cases with critical disease not in need of mechanical ventilation. Grade 4 was defined as being treated with mechanical ventilator support, thus corresponding to cases with WHO classification critical disease in need of mechanical ventilation.
Timepoints for comparison and handling of missing samples For five patients, samples corresponding to actual peak clinical severity were not available and, thus, these patients represented a lower grade of severity at this time point. However, since we at "Peak Severity" investigated the analyses' reflection of ongoing disease severity, this was insignificant. Seven patients were included in the study >7 days after initial hospitalization for different (random) reasons, and thus, baseline samples for both TruCulture and DuraClone were missing for these patients (four patients from Peak Severity Grade 1, three patients from Peak Severity Grade 3). Five additional patients did not have DuraClone samples collected in parallel with TruCulture samples at baseline, these samples were missing for random reasons (four patients from Grade 1, and one patient from Grade 2). At "Peak Severity", a DuraClone sample was missing at random for two patients (both from Grade 1). At "Discharge", the two patients with death as final outcome were not included. In addition, three patients did not have TruCulture samples and seven additional patients did not have DuraClone samples due to either rapid discharge after hospital admission or transfer to another hospital. Supplementary Table 14. P-values for associations between peak severity and covariates. Associations between peak severity and covariates at baseline (n=23), at/near peak severity (n=30), and discharge (n=25), assessed by univariate linear regression analyses for "Age", "Sex", "Days in hospital" (days from hospitalization to sample taken), and "Immune suppression" (presence of an immunosuppressive precondition), and by multivariate linear regression analyses for "Admission hospital (1-3)". Due to only one observation from Admission hospital 3, p-values for this variable were not available (NA).
Supplementary Even distribution of immune suppressive precondition across severity groups

Inclusion in COVIMUN study
April 2020 -October 2020 October 2020 -January 2021 Supplementary Figure 1. Severity at time of sample collection with corresponding peak severity and outcome, and distribution of immunosuppressive conditions and comorbidities across severity groups. a-c, Sankey plots illustrating severity grade at time of sample collection with corresponding peak severity for (a) baseline samples (n=23), (b) samples collected at/near peak severity (n=30), and (c) samples collected at discharge (n=25). d-f, Percentage of patients (n=30) across peak severity groups with (d) a pre-existing immunosuppressive condition, (e) at least one comorbidity, (f) two or more comorbidities. The percentage of patients is presented on the y-axis. The x-axis shows the groups of patients with peak severity grade 1 (1; n=15), grade 2 (2; n=5), grade 3 (3; n=6), and grade 4 (4; n=4). Bars are split into the number of patients represented in each group.     Figure 3. Poly:IC stimulated immune responses at baseline and at/near peak severity, and immune responses to all stimuli and immune cell constitution at discharge. a-b, Cytokine levels in response to Poly:IC at (a) baseline (n=23), and (b) at/near peak severity (n=30). Patients are grouped based on peak severity grade at baseline (Grade 1 (n=11, green), Grade 2 (n=5, yellow), Grade 3 (n=3, orange), Grade 4 (n=4, red)) and grouped based on severity at time of sample collection at/near peak severity (Grade 1 (n=16, green), Grade 2 (n=7, yellow), Grade 3 (n=5, orange), Grade 4 (n=2, red)). c, Cytokine levels in response to no stimulation, LPS, R848, CD3/CD28, and Poly:IC at discharge, corresponding to the sample closest to discharge for each patient (n=25, except for CD3/CD28: n=24). Patients are grouped based on previous peak severity grade: Grade 1 (n=13, CD3/CD28: n=12, green), Grade 2 (n=5, yellow), Grade 3 (n=6, orange), Grade 4 (n=1, red). d, Immune cell subset counts at discharge (n=18). Patients are grouped based on previous peak severity: Grade 1 (n=9, green), Grade 2 (n=4, yellow), Grade 3 (n=4, orange), Grade 4 (n=1, red). Cytokine levels and immune cell subset counts are presented on a log10 y-axis. Box edges represent the 25 th and 75 th percentiles, whiskers extend towards the most extreme values but no further than +/-1.5 times the interquartile range from the hinge. Hollow dots beyond whiskers represent outliers. Solid dots represent individual patient measurements. Blue shaded areas represent the normal reference interval. Grey boxplots in background represent baseline levels. Poly:IC, polyinosinic:polycytidylic acid; IFN, interferon; IL, interleukin; TNF, tumor necrosis factor; L O2, liters/minute of oxygen supply; ICU, intensive care unit; LPS, lipopolysaccharide; R848, resiquimod; CD, cluster of differentiation; NK, natural killer.            Associations between individual cytokine stimulus-response variables and current severity at/near peak severity. a-e, Associations after adjusting for age between individual cytokine variables (log-transformed cytokine concentration, log(concentration)) at/near peak severity and current severity grade (Peak Severity) for (a) LPS stimulation, (b) R848 stimulation, (c) no stimulation, (d) CD3/CD28 stimulation, (e) Poly:IC stimulation. Shaded areas behind regression lines represent 95% confidence intervals. f, p-values from all linear regression analyses (n=45) after adjusting for age on a -log10-axis at/near peak severity. The threshold for statistical significance is shown before adjusting for multiple tests (p=0.05, blue line) and after Bonferroni-adjustment (45 tests: p=0.001, red line). Only associations with p-values smaller than the Bonferroni corrected threshold (p<0.001) were considered statistically significant. g, Collected regression coefficient estimates for the stimulated cytokine variables and age by all stimuli at/near peak severity, hollow dots represent the estimates, bars represent 95% confidence intervals. LPS, lipopolysaccharide; R848, resiquimod; CD, cluster of differentiation; Poly:IC, polyinosinic:polycytidylic acid; IFN, interferon; IL, interleukin; TNF, tumor necrosis factor, L O2, liters/minute of oxygen supply; ICU, intensive care unit.    Supplementary Figure 8. Principal component analyses of TruCulture data at baseline, and ordinary least squares in combination with LASSO penalties to identify a baseline signature for predicting peak severity. a, Principal component analysis displaying severity levels based on LPS+R848 TruCulture baseline data. b, Principal component analysis displaying TruCulture data from all five stimuli at baseline. Arrows extend from origin to centroid of each stimulus representing the loadings of each stimuli in the projected dimensions. c, R 2 of the best model from each bin tested. d, Best lambda from each bin tested. e, The cross validation process for identifying the best lambda for the model based on combined LPS+R848 baseline data. f, The coefficients for the variables in the LPS+R848 model. Coefficients = 0 were considered insignificant for the model and thus excluded, all other coefficients were included. g, R 2 from all bins tested with and without including "Age" as a variable; red bars represent models excluding "Age", blue bars represent models including "Age". h, Best lambda from all bins tested with and without including "Age" as a variable; red bars represent models excluding "Age", blue bars represent models including "Age".  Figure 9. Stimulated immune responses by all stimuli at baseline. a, Cytokine variables by each stimulus separately at baseline. Each column represents a patient, each row represents a stimulus-cytokine variable. Columns are grouped by future peak severity, rows are grouped by inclusion/exclusion in the corresponding LASSO regression model. Hierarchical clustering by Euclidean distance as dissimilarity metric was preformed within groups/splits (dendrogram only shown for rows). The top annotations represent (up-down): "Sev": future peak severity, "Pred.Sev": predicted severity in current cohort based on the LPS+R848 LASSO regression model, "Sex": sex at birth, "Age": age at time of inclusion in study, "Imm.Sup": whether an immune-suppressive pre-condition was present, "Centroid": the row mean value. Row annotations represent (left-right): "Model": Inclusion/exclusion of variable in the LPS+R848 model, "Stimuli": the stimulus for each cytokine variable. Data used for visualization were log-transformed and standardized. LPS, lipopolysaccharide; R848, resiquimod; CD, cluster of differentiation; Poly:IC, polyinosinic:polycytidylic acid; L O2, liters/minute of oxygen supply; ICU, intensive care unit.  Figure 10. Validation of the immune response signature in a separate cohort. Predictions were performed using the LPS+R848 model based on baseline TruCulture cytokine concentration data (standardized and log-transformed) from a separate validation cohort of hospitalized COVID-19 patients (n=20, 5 in each peak severity group). a-d, Calculations of sensitivity/recall, specificity, false positive rate, false negative rate, positive predictive value/precision, negative predictive value, false discovery rate, false omission rate, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and Mathew's correlation coefficient are presented for predicting (a) severity grade 3-4 vs 1-2, (b) severity grade 1-2 vs 3-4, (c) severity grade 1 vs 2-4, and (d) severity grade 4 vs 1-3.
Predicting severity grade 3-4 vs 1-2 Supplementary Figure 10 a b c d